An Effective Representation Scheme in Multifactorial Evolutionary Algorithm for Solving Cluster Shortest-Path Tree Problem

Thành, Phạm Đình; Dũng, Đặng Đức; Tien, Tran Ngoc; Bình, Huỳnh Thị Thanh

doi:10.1109/cec.2018.8477684

Cited by 27 publications

(19 citation statements)

References 17 publications

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“…Each task T j is considered as a factor affecting individual evolution in the K-factorial environment [4,5]. MFEA is a popular implementation that integrates genetic operators in genetic algorithm into multifactorial optimization (Gupta et al, 2016b(Gupta et al, , 2017Bali et al, 2017;Feng et al, 2017;Wen and Ting, 2017;Binh et al, 2018Binh et al, , 2019Thanh et al, 2018;Zhong et al, 2018;Zhou et al, 2018Zhou et al, , 2019; Liang Shang et al, 2019;Yin et al, 2019;Yu et al, 2019;Zheng et al, 2019). All the individuals are encoded into a unified search space Y, and each individual can be decoded to optimize different component problems to effectively realize cross-domain knowledge transfer.…”

Section: Multifactorial Evolutionary Algorithmmentioning

confidence: 99%

“…Currently, the research on EMT can approximately be summarized into three categories, the practical application of EMT (Sagarna and Ong, 2016;Yuan et al, 2016;Zhou et al, 2016;Cheng et al, 2017;Binh et al, 2018;Thanh et al, 2018;Lian et al, 2019;Wang et al, 2019) and the improved algorithm based on the MFEA framework (Bali et al, 2017;Feng et al, 2017;Wen and Ting, 2017;Joy et al, 2018;Tuan et al, 2018;Zhong et al, 2018;Binh et al, 2019;Liang et al, 2019;Yin et al, 2019;Yu et al, 2019;Zheng et al, 2019;Zhou et al, 2019) and the perfection of EMT theory (Gupta et al, 2016a;Hashimoto et al, 2018;Liu et al, 2018;Zhou et al, 2018;Bali et al, 2019;Chen et al, 2019;Feng et al, 2019;Huang et al, 2019;Shang et al, 2019;Song et al, 2019;Tang et al, 2019). From the above studies, a consensus can be summarized that efficiently utilizing the inter-task related information is the key to improve overall search efficiency in EMT.…”

Section: Introductionmentioning

confidence: 99%

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A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Zhang

et al. 2020

Front. Neurorobot.

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In recent years, lots of multifactorial optimization evolutionary algorithms have been developed to optimize multiple tasks simultaneously, which improves the overall efficiency using implicit genetic complementarity between different tasks. In this paper, a novel multitask fireworks algorithm is proposed with novel transfer sparks to solve multitask optimization problems. For each task, some transfer sparks would be generated with adaptive length and promising direction vector, which are very helpful to transfer useful genetic information between different tasks. Finally, the proposed algorithm is compared against some chosen state-of-the-art evolutionary multitasking algorithms. The experimental results show that the proposed algorithm provides better performance on several single objectives and multiobjective MTO test suites.

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Section: Multifactorial Evolutionary Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Zhang

et al. 2020

Front. Neurorobot.

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“…The most widely utilized one is probably genetic mechanisms, namely crossover and mutation. Specifically, several typical genetic strategies include SBX [6,17], ordered crossover [25], one-point crossover [26], guided differential evolutionary crossover [27], Gaussian mutation [6], swap mutation [25], polynomial mutation [17,28], and swap-change mutation [29]. The other three EAs, differential evolution (DE) [13,[30][31][32], particle swarm optimization (PSO) [31,[33][34][35], and genetic programming (GP) [23], are also utilized as fundamental algorithm for MTO paradigms.…”

Section: Multi-population Evolution Modelmentioning

confidence: 99%

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

Wang¹,

Xu²,

Rong³

et al. 2019

IJCIS

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A B S T R A C TMulti-task optimization algorithm is an emergent paradigm which solves multiple self-contained tasks simultaneously. It is thought that multi-factorial evolutionary algorithm (MFEA) can be seen as a novel multi-population algorithm, wherein each population is represented independently and evolved for the selected task only. However, the theoretical and experimental evidence to this conclusion is not very convincing and especially, the coincidence relation between MFEA and multi-population evolution model is ambiguous and inaccurate. This paper aims to make an in-depth analysis of this relationship, and to provide more theoretical and experimental evidence to support the idea. In this paper, we clarify several key issues unsettled to date, and design a novel across-population crossover approach to avoid population drift. Then MFEA and its variation are reviewed carefully in view of multi-population evolution model, and the coincidence relation between them are concluded. MFEA is completely recoded along with this idea and tested on 25 multi-task optimization problems. Experimental results illustrate its rationality and superiority. Furthermore, we analyze the contribution of each population to algorithm performance, which can help us design more efficient multi-population algorithm for multi-task optimization. MTO paradigm [6]. If the optimization tasks happen to bear some commonality or complementarity, then the inclusion of knowledge transfer often leads to significant performance improvements relative to conventional EAs alone.

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“…The same authors, in an extended version of their paper [7], investigated the computational hardness, and provided some approximation results for both cases of the problem: unweighted and weighted. Binh et al [1] and Thanh et al [22] presented two multifactorial evolutionary algorithms that use different ways to encode feasible solutions of the CluSPTP: one based on the Cayley code and the other one using an edge set representation. Thanh et al [23] described a random heuristic search algorithm that combines a randomized greedy algorithm with a shortest path tree algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Effective Genetic Algorithm for Solving the Clustered Shortest-Path Tree Problem

2021

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The clustered shortest-path tree problem (CluSPTP) is an extension of the classical singlesource shortest-path problem, in which, given a graph with the set of nodes partitioned into a predefined, mutually exclusive and exhaustive set of clusters, we are looking for a shortest-path spanning tree from a given source to all the other nodes of the graph, with the property that each cluster should induce a connected subtree. CluSPTP belongs to the class of generalized combinatorial optimization problems, and, in general, is proved to be a non-deterministic polynomial time hard (NP-hard) problem. In this paper, we propose a novel genetic algorithm (GA), which is designed to fit the challenges of the investigated problem. The main features of our GA are: the use of an innovative representation scheme that allows us to define meaningful genetic operators and the use of a hybrid initial population. Extensive computational results are reported and discussed for two sets of instances: euclidean and non-euclidean. The performance of the proposed algorithm was evaluated on six types of benchmark euclidean instances available in the literature and on six types of non-euclidean instances obtained from the corresponding euclidean ones. The obtained results show an improvement with respect to existing methods from the literature, both in terms of the quality of the achieved solutions and the computation times necessary to obtain them. They demonstrate that our genetic algorithm outperforms all the existing methods from the literature, providing for all the existing benchmark instances the optimal solutions in all 30 independent trials.

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An Effective Representation Scheme in Multifactorial Evolutionary Algorithm for Solving Cluster Shortest-Path Tree Problem

Cited by 27 publications

References 17 publications

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

A Fireworks Algorithm Based on Transfer Spark for Evolutionary Multitasking

Rigorous Analysis of Multi-Factorial Evolutionary Algorithm as Multi-Population Evolution Model

An Effective Genetic Algorithm for Solving the Clustered Shortest-Path Tree Problem

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